Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks

The increasing complexity of stock market predictions necessitates advanced computational techniques to address the unique challenges posed by financial data's non-linear and volatile nature. This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. The LSTM model excels at processing sequential time-series data, allowing it to predict monthly stock closing prices over a one-year horizon with a high degree of precision. Our findings indicate a Root Mean Squared Error (RMSE) of 3.2, underscoring the model's efficiency and reliability in financial forecasting tasks. The novelty of this research lies in the systematic incorporation of preprocessing techniques and fine-tuned hyperparameters to optimize model performance. Furthermore, this study explores the practical implications of implementing LSTM models in real-world trading scenarios, analyzing their adaptability to dynamic market conditions and their potential integration into automated trading systems. These findings contribute to the growing body of knowledge in financial analytics and demonstrate the viability of machine learning-based solutions for accurate and robust market predictions.